Here, we analyze frequency data from Continental Europe from 2011 and 2017 as a case study. We first observe the impact of trading based on hourly mean frequency trajectories and investigate the aggregated distribution of frequency values. To isolate effects by trading, we split the given data set into time windows surrounding the 15-minutes trading intervals. Evaluating measures such as standard deviations and kurtosis, we quantify differences between trading and non-trading time intervals of the time series.
Finally, we briefly review important stochastic results on quantifying frequency distributions [1]}.
